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Author SHA1 Message Date
0ac2c57638 fix: deduplicate shebang in glitch_patterns.py (#685) 2026-04-15 03:09:25 +00:00
b46545488a fix: add python3 shebang to glitch_patterns.py (#685) 2026-04-15 03:07:21 +00:00
f5d0b0efd4 fix: add python3 shebang to glitch_patterns.py (#685)
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2026-04-15 03:05:53 +00:00
2 changed files with 6 additions and 6 deletions

View File

@@ -1,3 +1,4 @@
#!/usr/bin/env python3
"""
Glitch pattern definitions for 3D world anomaly detection.

View File

@@ -15,7 +15,6 @@
MODEL ?= timmy:v0.1-q4
BASELINE ?= hermes3:latest
OLLAMA_URL ?= http://localhost:11434
PYTHON ?= python3
OUTPUT ?= output
# ── Training ──────────────────────────────────────────────────────────
@@ -24,7 +23,7 @@ train-cloud: ## QLoRA fine-tune on cloud GPU (Axolotl)
axolotl train axolotl.yaml
train-local: ## LoRA fine-tune on Apple Silicon (MLX)
$(PYTHON) -m mlx_lm.lora --config mlx-lora.yaml
python -m mlx_lm.lora --config mlx-lora.yaml
# ── Evaluation ────────────────────────────────────────────────────────
@@ -46,7 +45,7 @@ vibes: ## Run vibes check — hand-picked prompts, human review
@echo "Date: $$(date '+%Y-%m-%d %H:%M')" > $(OUTPUT)/vibes-$(MODEL).md
@echo "Model: $(MODEL)" >> $(OUTPUT)/vibes-$(MODEL).md
@echo "" >> $(OUTPUT)/vibes-$(MODEL).md
@$(PYTHON) -c "\
@python -c "\
import yaml, subprocess, sys; \
prompts = yaml.safe_load(open('data/prompts_vibes.yaml'))['prompts']; \
f = open('$(OUTPUT)/vibes-$(MODEL).md', 'a'); \
@@ -70,19 +69,19 @@ vibes: ## Run vibes check — hand-picked prompts, human review
# ── Data Pipeline ─────────────────────────────────────────────────────
ingest: ## Pull heartbeat trajectories into training data
$(PYTHON) ingest_trajectories.py \
python ingest_trajectories.py \
--trajectories ~/.nexus/trajectories/ \
--curated data/curated_dataset.jsonl \
--output data/merged_training_data.jsonl
@echo "Merged dataset ready. Convert for MLX with: make convert"
curated: ## Regenerate curated exemplar dataset
$(PYTHON) build_curated.py
python build_curated.py
@echo "Curated dataset regenerated."
convert: ## Convert merged dataset to MLX format (train/valid split)
@mkdir -p data/mlx_curated
$(PYTHON) -c "\
python -c "\
import json; \
lines = open('data/merged_training_data.jsonl').readlines(); \
sessions = [json.loads(l) for l in lines]; \